A Field Study of Related Video Recommendations: Newest, Most Similar, or Most Relevant?

Yifan Zhong, Tahir Lazaro Sousa Menezes, Vikas Kumar, Qian Zhao, F Maxwell Harper

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Scopus citations

Abstract

Many video sites recommend videos related to the one a user is watching. These recommendations have been shown to influence what users end up exploring and are an important part of a recommender system. Plenty of methods have been proposed to recommend related videos, but there has been relatively little work that compares competing strategies. We describe a field study of related video recommendations, where we deploy algorithms to recommend related movie trailers. Our results show that recency- and similarity-based algorithms yield the highest click-through rates, and that the recency-based algorithm leads to the most trailer-level engagement. Our findings suggest the potential to design non-personalized yet effective related item recommendation strategies.
Original languageEnglish (US)
Title of host publicationProceedings of the 12th ACM Conference on Recommender Systems
PublisherACM
Pages274-278
Number of pages5
ISBN (Print)978-1-4503-5901-6
DOIs
StatePublished - 2018

Publication series

NameProceedings of the 12th ACM Conference on Recommender Systems

Keywords

  • field study
  • item similarity
  • movie trailers
  • recommender systems
  • related item recommendations

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